Quantum sensing has emerged as a promising solution for precision measurement, and distributed quantum sensing enables multiple parameters to be measured across a network. Pushing distributed quantum sensing from the single to multimodal parameters is crucial for a complete and deep understanding of a complex system. To address this challenge, we have devised a high-sensitivity distributed quantum sensing of the multimodal parameters with a continuous-variable multipartite Greenberger–Horne–Zeilinger entangled state. All the sensors are equipped with Mach–Zehnder interferometers to couple the lasers with entangled modes. In such a quantum network, not only joint but also individual measurements of radio frequency phases and amplitudes as well as ultrasonic vibration signals are achieved beyond the standard quantum limit, showcasing the quantum Fisher information of the order of 10 16 . These results render multimodal parameters a promising contender in quantum metrology.
Quantum sensing has emerged as a promising solution for precision measurement, and distributed quantum sensing enables multiple parameters to be measured across a network. Pushing distributed quantum sensing from the single to multimodal parameters is crucial for a complete and deep understanding of a complex system. To address this challenge, we have devised a high-sensitivity distributed quantum sensing of the multimodal parameters with a continuous-variable multipartite Greenberger–Horne–Zeilinger entangled state. All the sensors are equipped with Mach–Zehnder interferometers to couple the lasers with entangled modes. In such a quantum network, not only joint but also individual measurements of radio frequency phases and amplitudes as well as ultrasonic vibration signals are achieved beyond the standard quantum limit, showcasing the quantum Fisher information of the order of 10 16 . These results render multimodal parameters a promising contender in quantum metrology.
Quantum metrology takes advantage of nonclassical resources such as squeezing and entanglement to achieve a sensitivity level below the standard quantum limit (SQL). To date, most demonstrations of quantum sensing are restricted to a single sensor, nevertheless, many sensing tasks rely on an array of sensors working collectively. The measurement sensitivity of separable sensors scales as 1/sqrt(M) known as the SQL where M is the number of sensors. Shared entanglement among all sensors can be harnessed to surpass the SQL. Recent theoretical advances in distributed quantum sensing have shown that multipartite entangled states give rise to an improvement of 1/sqrt(M) in measurement sensitivity over separable states when estimating a global parameter. We develop a reconfigurable entangled sensor network based on continuous variable multipartite entangled states. The demonstrated entangled sensor network is composed of three sensor nodes, each equipped with an electro-optic transducer for the detection of radio-frequency (RF) signals. By properly tailoring the entangled states through a variational quantum circuit (VQC), the entangled sensor network can be reconfigured to minimize the measurement noise by more than 3dB below the SQL in different distributed RF sensing tasks, e.g., measuring the angle of arrival and mean amplitude of incident RF waves. Such a capability of capturing global features of interrogating objects by a reconfigurable entangled sensor network with less quantum noise further creates opportunities to enable a quantum advantage in data-processing problems. VQCs in conjunction with classical processing constitutes a promising architecture for quantum simulations, classical optimization, and machine learning. We train the VQC by classical machine learning algorithms to optimize the entanglement shared by the sensors for solving practical data processing problems. We show an entanglement-enabled reduction in error probability for classification of multidimensional RF signals. Our work establishes the quantum advantage of distributed quantum sensing and would lead to applications in ultrasensitive positioning, navigation, and data processing.
Distributed quantum sensing, which estimates a global parameter across distant nodes, has attracted significant interest for applications such as quantum imaging, sensor networks, and global-scale clock synchronization. N00N states are regarded as one of the optimal quantum resources for quantum metrology, enabling the Heisenberg scaling. Recently, the concept of N00N states has been extended to multimode N00N states for quantum-enhanced multiple-parameter estimation. However, the application of multimode N00N states in distributed quantum sensing remains unexplored. Here, we propose a distributed quantum sensing scheme that achieves the Heisenberg scaling using multimode N00N states. We theoretically show that multimode N00N states can reach the Heisenberg scaling by examining both the Cramér-Rao bound and the quantum Cramér-Rao bound. For experimental demonstration, we employ a four-mode 2002 state to estimate the average of two spatially distributed phases, achieving a 2.74 dB sensitivity enhancement over the standard quantum limit. We believe that utilizing multimode N00N states for distributed quantum sensing offers a promising approach for developing entanglement-enhanced sensor networks.
Abstract Quantum networks can enhance both security and privacy conditions for multi-user communication, delegated computation, and distributed sensing tasks. In distributed quantum sensing, it is often desirable to extract only global information from a network of sensors while keeping individual sensor values hidden. Private parameter estimation (PPE), a recently proposed protocol, formalises this requirement by defining a notion of privacy that bounds information leakage about local parameters while allowing estimation of a global function. Here, we present a proof-of-principle implementation of PPE using a three-photon Greenberger–Horne–Zeilinger state distributed across three sensors and verified via stabiliser measurements. We demonstrate Heisenberg-limited scaling for the global parameter while suppressing local metrological information by up to three orders of magnitude, establishing both the privacy and precision performance of the protocol. This work, which integrates privacy in distributed quantum sensing, marks a crucial step towards developing advanced quantum-secure-and-private protocols in complex quantum networks.
Distributed quantum sensing has important applications in quantum-enhanced estimation of global parameters, which is the weighted sum of each sensor. Towards applications, it is essential to estimate more global parameters simultaneously. Here, we propose an improved quantum-enhanced distributed multi-parameter sensing of more than two global parameters simultaneously via Gaussian multipartite entanglement. We show that two global parameters, containing the real and imaginary parts of the complex amplitudes of multiple radio-frequency fields, can be estimated based on an Einstein-Podolsky-Rosen entangled state. Compared to the distributed quantum sensing with squeezed light in the same sensing network, the sensors are extended from M to 2 M in the improved scheme with multipartite entanglement. This work provides a practical approach for advancing high-precision multi-parameter quantum sensing technologies.
Distributed quantum sensing leverages quantum correlations among multiple sensors to enhance the precision of parameter estimation beyond classical limits. Most existing approaches target phase estimation and rely on a shared phase reference between the signal and the probe, yet many relevant scenarios deal with regimes where such a reference is absent, making the estimation of force or field amplitudes the main task. We study this phase-insensitive regime for bosonic sensors that undergo identical displacements with common phases randomly varying between experimental runs. We derive analytical bounds on the achievable precision and show that it is determined by first-order normal correlations between modes in the probe state, constrained by their average excitations. These correlations yield a collective sensitivity enhancement over the standard quantum limit, with a gain that grows linearly in the total excitation number, revealing a distributed quantum advantage even without a global phase reference. We identify families of multimode states with definite joint parity that saturate this limit and can be probed efficiently via local parity measurements already demonstrated or emerging in several quantum platforms. We further demonstrate that experimentally relevant decoherence channels favor two distinct sensing strategies: splitting of a single-mode nonclassical state among the modes, which is robust to loss and heating, and separable probes, which are instead resilient to dephasing and phase jitter. Our results are relevant to multimode continuous platforms, including trapped-ion, solid-state mechanical, optomechanical, superconducting, and photonic systems.
Distributed quantum sensing exploits entanglement to enhance the estimation of multiple parameters across a network of spatially-separated sensors, achieving sensitivities beyond the classical limit. Potential applications cover a plethora of technologies, from precision navigation to biomedical imaging and environmental monitoring. However, practical implementations are challenged by the complex optimal distribution of entanglement throughout the sensing nodes, which affects scalability and robustness. Here we demonstrate a reconfigurable network of Mach-Zehnder interferometers entangled via a single shared squeezed-vacuum resource. We achieve joint noise suppression of 4.36±0.35 dB below the standard quantum limit at the phase-uncertainty level of 10−9 . Furthermore, after full optimization in the low-intensity regime, we demonstrate a crossover from the standard quantum limit to the Heisenberg limit. The network estimates arbitrary linear combinations of phases, saturates the quantum Cramer-Rao bound in the ideal case, remains robust under realistic photon losses, and scales favorably with the number of sensors.